FP4S: Fragment-based Parallel State Recovery for Stateful Stream Applications

Abstract

Streaming computations are by nature long-running. They run in highly dynamic distributed environments where many stream operators may leave or fail at the same time. Most of them are stateful, in which stream operators need to store and maintain large-sized state in memory, resulting in expensive time and space costs to recover them. The state-of-the-art stream processing systems offer failure recovery mainly through three approaches: replication recovery, checkpointing recovery, and DStream-based lineage recovery, which are either slow, resource-expensive or fail to handle many simultaneous failures.We present FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications. The novelty of FP4S is that we organize all the application’s operators into a distributed hash table (DHT) based consistent ring to associate each operator with a unique set of neighbors. Then we divide each operator’s in-memory state into many fragments and periodically save them in each node’s neighbors, ensuring that different sets of available fragments can reconstruct lost state in parallel. This approach makes this failure recovery mechanism extremely scalable, and allows it to tolerate many simultaneous operator failures. We apply FP4S on Apache Storm and evaluate it using large-scale real-world experiments, which demonstrate its scalability, efficiency, and fast failure recovery features. When compared to the state-of-the-art solutions (Apache Storm), FP4S reduces 37.8% latency of state recovery and saves more than half of the hardware costs. It can scale to many simultaneous failures and successfully recover the states when up to 66.6% of states fail or get lost.